Paula Tanabe, PhD, RN, FAEN, FAAN
Laurel B. Chadwick Distinguished Professor of Nursing
Duke University
Talk Title: A Public Health Led, Statewide Approach to Improving Emergency Department Care for Individuals with Sickle Cell Disease in North Carolina

Persons with sickle cell disease (SCD) suffer from acute painful episodes that often require treatment with opioids in the emergency department (ED); they are often stigmatized and receive poor pain management. This session will report ongoing efforts in North Carolina to systematically address gaps in SCD care in the ED. The role of the North Carolina Governor's Council on SCD and Other blood disorders will be discussed. The session will illustrate how health systems, community-based organizations, and state health departments can work together to develop strategies and resources to improve ED care for SCD.

Marina Sirota, PhD
Professor and Interim Director, Bakar Computational Health Sciences Institute
University of California San Francisco
Talk Title: Leveraging Electronic Medical Records and Machine Learning Approaches to Study Endometriosis in Diverse Populations

Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyze comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data. We find hundreds of conditions significantly associated with endometriosis, including genitourinary disorders, neoplasms, and autoimmune diseases, with strong replication across datasets. Clustering analyses identify patient subpopulations with distinct comorbidity patterns, including psychiatric and autoimmune conditions. This study provides a comprehensive analysis of endometriosis comorbidities and highlights the heterogeneity within the patient population. Our findings demonstrate the utility of EHR data in uncovering clinically meaningful patterns and suggest pathways for personalized disease management and future research on biological mechanisms underlying endometriosis.